U.S. patent number 10,366,406 [Application Number 14/563,858] was granted by the patent office on 2019-07-30 for estimating the reach performance of an advertising campaign.
This patent grant is currently assigned to Facebook, Inc.. The grantee listed for this patent is Facebook, Inc.. Invention is credited to Charles Joseph Hughes, Patrick Brendan Maginnis, Xiao Zhang.
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United States Patent |
10,366,406 |
Hughes , et al. |
July 30, 2019 |
Estimating the reach performance of an advertising campaign
Abstract
A social networking system receives information describing
locations associated with a plurality of its users. Based on
information identifying each user and a location associated with
each user, the social networking system generates and stores hash
values. For example, the social networking system maintains various
geo-tiles that each identify geographic areas and generates a hash
value based on a user identifier and an identifier of a geo-tile
including the location associated with the user. Based on the hash
values and locations associated with one or more users, the online
system determines a number of unique users associated with
locations included in a geographic region. When determining the
number of unique users, the online system accounts for a rate at
which the online system updates location information associated
with various users.
Inventors: |
Hughes; Charles Joseph (San
Francisco, CA), Zhang; Xiao (Mountain View, CA),
Maginnis; Patrick Brendan (San Francisco, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Facebook, Inc. |
Menlo Park |
CA |
US |
|
|
Assignee: |
Facebook, Inc. (Menlo Park,
CA)
|
Family
ID: |
56094693 |
Appl.
No.: |
14/563,858 |
Filed: |
December 8, 2014 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20160162932 A1 |
Jun 9, 2016 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q
30/0246 (20130101); G06Q 30/0261 (20130101); H04L
67/02 (20130101); H04L 67/535 (20220501); H04L
67/306 (20130101); H04L 67/53 (20220501); H04L
67/52 (20220501); G06Q 30/0205 (20130101); H04L
67/125 (20130101) |
Current International
Class: |
G06Q
30/02 (20120101); H04L 29/08 (20060101) |
Field of
Search: |
;705/14.1-14.73 |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Refai; Sam
Attorney, Agent or Firm: Fenwick & West LLP
Claims
What is claimed is:
1. A method comprising: receiving information describing locations
of a plurality of users; updating locations associated with each of
the plurality of users based on the received information;
generating cryptographic hash values for each of the plurality of
users, a hash value for a user based at least in part on received
information describing a location of the user and information
identifying the user; storing the generated hash values and an
association between the generated hash value and a corresponding
location; identifying a geographic region including a set of
geo-tiles, each geo-tile identifying a portion of the geographic
region; determining a set of distinct users based at least in part
on the generated hash values, each distinct user having a location
within a geo-tile included in the identified geographic region;
determining an average sampling rate for the set of geo-tiles based
at least in part on a number of hash values associated with
locations within each of the geo-tiles in the set of geo-tiles and
a number of distinct users associated with locations within each of
the geo-tiles in the set of geo-tiles; and determining an estimated
number of users associated with locations within the identified
geographic region based on a number of users in the set of distinct
users and the determined average sampling rate for the set of
geo-tiles.
2. The method of claim 1, wherein determining the average sampling
rate for the set of geo-tiles comprises: determining a sampling
rate for each a plurality of subsets of geo-tiles of the set of
geo-tiles included in the geographic region; and determining the
average sampling rate for the set of geo-tiles based on the
sampling rates for each of the plurality of subsets of
geo-tiles.
3. The method of claim 1, wherein determining the average sampling
rate for the set of geo-tiles comprises: determining a number of
geo-tiles in the set of geo-tiles included in the geographic
region; determining a geo-tile resolution based on the determined
number of geo-tiles in the set of geo-tiles; determining a set of
geo-tiles having the determined geo-tile resolution and including
the geo-tiles in the set of geo-tiles included in the geographic
region; and determining an average sampling rate for the set of
geo-tiles having the determined geo-tile resolution and including
the geo-tiles in the set of geo-tiles included in the geographic
region.
4. The method of claim 3, wherein determining the average sampling
rate for the set of geo-tiles further comprises: determining an
additional geo-tile resolution based on the determined number of
geo-tiles in the set of geo-tiles; determining an additional set of
geo-tiles having the additional determined geo-tile resolution and
including the geo-tiles in the set of geo-tiles included in the
geographic region; determining an additional average sampling rate
for the additional set of geo-tiles having the additional
determined geo-tile resolution and including the geo-tiles in the
set of geo-tiles included in the geographic region; and determining
the average sampling rate based on the average sampling rate for
the set of geo-tiles having the determined geo-tile resolution and
including the geo-tiles in the set of geo-tiles included in the
geographic region and the additional average sampling rate for the
additional set of geo-tiles having the additional determined
geo-tile resolution and including the geo-tiles in the set of
geo-tiles included in the geographic region.
5. The method of claim 4, wherein determining the average sampling
rate based on the average sampling rate for the set of geo-tiles
having the determined geo-tile resolution and including the
geo-tiles in the set of geo-tiles included in the geographic region
and the additional average sampling rate for the additional set of
geo-tiles having the additional determined geo-tile resolution and
including the geo-tiles in the set of geo-tiles included in the
geographic region comprises: associating weights with the average
sampling rate for the set of geo-tiles having the determined
geo-tile resolution and including the geo-tiles in the set of
geo-tiles included in the geographic region and the additional
average sampling rate for the additional set of geo-tiles having
the additional determined geo-tile resolution and including the
geo-tiles in the set of geo-tiles included in the geographic region
based at least in part on the number of geo-tiles in the set of
geo-tiles included in the geographic region; and determining the
average sampling rate based at least in part on the weights.
6. The method of claim 4, wherein determining the geo-tile
resolution based on the determined number of geo-tiles in the set
of geo-tiles comprises: determining a resolution of the geo-tiles
in the set of geo-tiles; determining a value based on the number of
geo-tiles in the set of geo-tiles included in the geographic
region; and determining the geo-tile resolution based on the
resolution of the geo-tile in the set of geo-tiles and the
determined value.
7. The method of claim 6, wherein determining the geo-tile
resolution based on the resolution of the geo-tile in the set of
geo-tiles and the determined value comprises: determining the
geo-tile resolution as a difference between the resolution of the
geo-tiles in the set of geo-tiles and the determined value.
8. The method of claim 6, wherein determining the additional
geo-tile resolution based on the determined number of geo-tiles in
the set of geo-tiles comprises: determining an alternative value
based on the a number of geo-tiles in the set of geo-tiles included
in the geographic region; and determining the additional geo-tile
resolution based on the resolution of the geo-tile in the set of
geo-tiles and the alternative value.
9. The method of claim 8, wherein determining the additional
geo-tile resolution based on the resolution of the geo-tiles in the
set of geo-tiles and the alternative value comprises: determining
the additional geo-tile resolution as a difference between the
resolution of the geo-tiles in the set of geo-tiles and the
alternative value.
10. The method of claim 9, wherein the determined value comprises
an integer value less than a logarithm of the number of determined
number of geo-tiles in the set of geo-tiles and wherein the
alternative value comprises an integer value greater than the
logarithm of the number of determined number of geo-tiles in the
set of geo-tiles.
11. The method of claim 1, wherein the estimated number of users
associated with locations within the identified geographic region
is a product of the number of users in the set of distinct users
and the determined average sampling rate for the set of
geo-tiles.
12. A non-transitory computer-readable storage medium having
instructions encoded thereon that, when executed by a processor,
cause the processor to: receive information describing locations of
a plurality of users; update locations associated with each of the
plurality of users based on the received information; generate
cryptographic hash values for each of the plurality of users, a
hash value for a user based at least in part on received
information describing a location of the user and information
identifying the user; store the generated hash values and an
association between the generated hash value and a corresponding
location; identify a geographic region including a set of
geo-tiles, each geo-tile identifying a portion of the geographic
region; determine a set of distinct users based at least in part on
the generated hash values, each distinct user having a location
within a geo-tile included in the identified geographic region;
determine an average sampling rate for the set of geo-tiles based
at least in part on a number of hash values associated with
locations within each of the geo-tiles in the set of geo-tiles and
a number of distinct users associated with a location within each
of the geo-tiles in the set of geo-tiles; and determine an
estimated number of users associated with locations within the
identified geographic region based on a number of users in the set
of distinct users and the determined average sampling rate for the
set of geo-tiles.
13. The non-transitory computer-readable storage medium of claim
12, wherein the estimated number of users associated with locations
within the identified geographic region is a product of the number
of users in the set of distinct users and the determined average
sampling rate for the set of geo-tiles.
14. The non-transitory computer-readable storage medium of claim
12, wherein determine the average sampling rate for the set of
geo-tiles comprises: determine a number of geo-tiles in the set of
geo-tiles included in the geographic region; determine a geo-tile
resolution based on the determined number of geo-tiles in the set
of geo-tiles; determine an additional geo-tile resolution based on
the determined number of geo-tiles in the set of geo-tiles;
determine a set of geo-tiles having the determined geo-tile
resolution and including the geo-tiles in the set of geo-tiles
included in the geographic region; determine an additional set of
geo-tiles having the additional determined geo-tile resolution and
including the geo-tiles in the set of geo-tiles included in the
geographic region; determine an average sampling rate for the set
of geo-tiles having the determined geo-tile resolution and
including the geo-tiles in the set of geo-tiles included in the
geographic region; determine an additional average sampling rate
for the additional set of geo-tiles having the additional
determined geo-tile resolution and including the geo-tiles in the
set of geo-tiles included in the geographic region; and determine
the average sampling rate based on the average sampling rate for
the set of geo-tiles having the determined geo-tile resolution and
including the geo-tiles in the set of geo-tiles included in the
geographic region and the additional average sampling rate for the
additional set of geo-tiles having the additional determined
geo-tile resolution and including the geo-tiles in the set of
geo-tiles included in the geographic region.
15. The non-transitory computer-readable storage medium of claim
14, wherein determine the average sampling rate based on the
average sampling rate for the set of geo-tiles having the
determined geo-tile resolution and including the geo-tiles in the
set of geo-tiles included in the geographic region and the
additional average sampling rate for the additional set of
geo-tiles having the additional determined geo-tile resolution and
including the geo-tiles in the set of geo-tiles included in the
geographic region comprises: associate weights with the average
sampling rate for the set of geo-tiles having the determined
geo-tile resolution and including the geo-tiles in the set of
geo-tiles included in the geographic region and the additional
average sampling rate for the additional set of geo-tiles having
the additional determined geo-tile resolution and including the
geo-tiles in the set of geo-tiles included in the geographic region
based at least in part on the number of geo-tiles in the set of
geo-tiles included in the geographic region; and determine the
average sampling rate based at least in part on the weights.
Description
BACKGROUND
This disclosure relates generally to online advertising, and more
specifically to estimating a number of users an online
advertisement may reach.
Advertisers may have certain goals when planning a new advertising
campaign ("advertising campaign"). For instance, an advertiser may
have a goal for the number of times an advertisement is presented
to users of an online system (i.e., the number of "impressions" of
the advertisement), the number of different online system users
presented with an advertisement (i.e., the "reach" of the
advertisement), and/or a duration during which the advertisement is
presented. Conventionally, an advertiser specifies certain
attributes of an advertising campaign or advertisements in an
advertising campaign when specifying the advertising campaign. For
example, an advertiser identifies targeting criteria associated
with one or more advertisements in an advertising campaign.
However, advertisers may lack information to accurately identify
how to achieve one or more goals of an advertising campaign. For
instance, an advertiser may have a desired reach for an
advertisement campaign, but lack information for determining a
geographic area in which the advertisement is presented that would
achieve the desired reach. Alternatively, an advertiser may specify
a geographic area in which an advertisement is presented, but lack
information for determining a reach of the advertisement within the
specified geographic area.
SUMMARY
A social networking system determines an estimated number of
distinct users capable of being presented with an advertisement
within a geographic location. The social networking system
maintains information associating a location with various users,
and also maintains various geo-tiles that each correspond to a
geographic area. Based on the locations associated with users, the
social networking system determines a number of distinct users
included in geo-tiles included in a geographic region associated
with an advertisement to estimate a number of distinct users
eligible to be presented with the advertisement.
Because of privacy concerns with storing a location history of a
user, the social networking system associates a user with a single
location, rather than a history of prior locations. However, the
social networking system stores cryptographic hash values generated
based on information identifying a user and information identifying
a geo-tile including a location associated with the user, while the
cryptographic hash value does not allow the social networking
system to subsequently identify the user, it allows the social
networking system to identify a number of users associated with a
location included in a geo-tile. An association between the
cryptographic hash value and the geo-tile used to generate the
cryptographic hash value is stored by the social networking system
along with the cryptographic hash value. To generate the
cryptographic hash value, a cryptographic hash function that does
not allow the social networking system to determine information
identifying the user from the cryptographic hash value is used.
Using the cryptographic hash values, a number of distinct users
associated with locations within a specific geo-tile is
determined.
Based on a number of users associated with locations included in a
geo-tile and the determined number of distinct users associated
with locations included in the geo-tile, a sampling rate for the
geo-tile is determined. Based on the sampling rates for each
geo-tile included in a geographic region, an average sampling rate
is determined. Using the average sampling rate and the number of
users associated with a location inside the geographic region, an
estimated number of distinct users associated with locations inside
the geographic region is determined.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of a system environment in which a social
networking system operates, in accordance with an embodiment.
FIG. 2 is a block diagram of a social networking system, in
accordance with an embodiment.
FIG. 3 is a table illustrating association between users and
geographic locations, in accordance with an embodiment.
FIG. 4 illustrates a process for increasing a probability of
updating a location associated with a user, in accordance with an
embodiment.
FIG. 5 illustrates values associated with geo-tiles by the social
networking system, in accordance with an embodiment.
FIG. 6 is a flowchart of a process for estimating a number of users
associated with locations within a geographic region, in accordance
with an embodiment.
FIG. 7 illustrates a geographic region and use of geo-tiles to
estimate a number of distinct social networking system users
associated with a location within the geographic region, in
accordance with an embodiment.
FIG. 8 illustrates a map of a geographic region and use of
geo-tiles with multiple levels of resolution to estimate a number
of distinct social networking system users associated with
locations within the geographic region, in accordance with an
embodiment.
The figures depict various embodiments for purposes of illustration
only. One skilled in the art will readily recognize from the
following discussion that alternative embodiments of the structures
and methods illustrated herein may be employed without departing
from the principles described herein.
DETAILED DESCRIPTION
System Architecture
FIG. 1 is a block diagram of a system environment 100 for a social
networking system 140. The system environment 100 shown by FIG. 1
comprises one or more client devices 110, a network 120, one or
more third-party systems 130, and the social networking system 140.
In alternative configurations, different and/or additional
components may be included in the system environment 100. The
embodiments described herein can be adapted to online systems that
are not social networking systems.
The client devices 110 are one or more computing devices capable of
receiving user input as well as transmitting and/or receiving data
via the network 120. In one embodiment, a client device 110 is a
conventional computer system, such as a desktop or a laptop
computer. Alternatively, a client device 110 may be a device having
computer functionality, such as a personal digital assistant (PDA),
a mobile telephone, a smartphone or another suitable device. A
client device 110 is configured to communicate via the network 120.
In one embodiment, a client device 110 executes an application
allowing a user of the client device 110 to interact with the
social networking system 140. For example, a client device 110
executes a browser application to enable interaction between the
client device 110 and the social networking system 140 via the
network 120. In another embodiment, a client device 110 interacts
with the social networking system 140 through an application
programming interface (API) running on a native operating system of
the client device 110, such as IOS.RTM. or ANDROID.TM..
The client devices 110 are configured to communicate via the
network 120, which may comprise any combination of local area
and/or wide area networks, using both wired and/or wireless
communication systems. In one embodiment, the network 120 uses
standard communications technologies and/or protocols. For example,
the network 120 includes communication links using technologies
such as Ethernet, 802.11, worldwide interoperability for microwave
access (WiMAX), 3G, 4G, code division multiple access (CDMA),
digital subscriber line (DSL), etc. Examples of networking
protocols used for communicating via the network 120 include
multiprotocol label switching (MPLS), transmission control
protocol/Internet protocol (TCP/IP), hypertext transport protocol
(HTTP), simple mail transfer protocol (SMTP), and file transfer
protocol (FTP). Data exchanged over the network 120 may be
represented using any suitable format, such as hypertext markup
language (HTML) or extensible markup language (XML). In some
embodiments, all or some of the communication links of the network
120 may be encrypted using any suitable technique or
techniques.
One or more third party systems 130 may be coupled to the network
120 for communicating with the social networking system 140, which
is further described below in conjunction with FIG. 2. In one
embodiment, a third party system 130 is an application provider
communicating information describing applications for execution by
a client device 110 or communicating data to a client device 110
for use by an application executing on the client device 110. In
other embodiments, a third party system 130 provides content or
other information for presentation via a client device 110. A third
party system 130 may also communicate information to the social
networking system 140, such as advertisements, content, or
information about an application provided by the third party system
130.
FIG. 2 is a block diagram of an architecture of the social
networking system 140. The social networking system 140 shown in
FIG. 2 includes a user profile store 205, a content store 210, an
action logger 215, an action log 220, an edge store 225, the
advertisement module 230, and a web server 270. In other
embodiments, the social networking system 140 may include
additional, fewer, or different components for various
applications. Conventional components such as network interfaces,
security functions, load balancers, failover servers, management
and network operations consoles, and the like are not shown so as
to not obscure the details of the system architecture.
Each user of the social networking system 140 is associated with a
user profile, which is stored in the user profile store 205. A user
profile includes declarative information about the user that was
explicitly shared by the user and may also include profile
information inferred by the social networking system 140. In one
embodiment, a user profile includes multiple data fields, each
describing one or more attributes of the corresponding social
networking system user. Examples of information stored in a user
profile include biographic, demographic, and other types of
descriptive information, such as work experience, educational
history, gender, hobbies or preferences, location and the like. A
user profile may also store other information provided by the user,
for example, images or videos. In certain embodiments, images of
users may be tagged with information identifying the social
networking system users displayed in an image. A user profile in
the user profile store 205 may also maintain references to actions
by the corresponding user performed on content items in the content
store 210 and stored in the action log 220.
While user profiles in the user profile store 205 are frequently
associated with individuals, allowing individuals to interact with
each other via the social networking system 140, user profiles may
also be stored for entities such as businesses or organizations.
This allows an entity to establish a presence on the social
networking system 140 for connecting and exchanging content with
other social networking system users. The entity may post
information about itself, about its products or provide other
information to users of the social networking system using a brand
page associated with the entity's user profile. Other users of the
social networking system may connect to the brand page to receive
information posted to the brand page or to receive information from
the brand page. A user profile associated with the brand page may
include information about the entity itself, providing users with
background or informational data about the entity.
The content store 210 stores objects that each represent various
types of content. Examples of content represented by an object
include a page post, a status update, a photograph, a video, a
link, a shared content item, a gaming application achievement, a
check-in event at a local business, a brand page, or any other type
of content. Social networking system users may create objects
stored by the content store 210, such as status updates, photos
tagged by users to be associated with other objects in the social
networking system 140, events, groups or applications. In some
embodiments, objects are received from third-party applications or
third-party applications separate from the social networking system
140. In one embodiment, objects in the content store 210 represent
single pieces of content, or content "items." Hence, social
networking system users are encouraged to communicate with each
other by posting text and content items of various types of media
to the social networking system 140 through various communication
channels. This increases the amount of interaction of users with
each other and increases the frequency with which users interact
within the social networking system 140.
The action logger 215 receives communications about user actions
internal to and/or external to the social networking system 140,
populating the action log 220 with information about user actions.
Examples of actions include adding a connection to another user,
sending a message to another user, uploading an image, reading a
message from another user, viewing content associated with another
user, and attending an event posted by another user. In addition, a
number of actions may involve an object and one or more particular
users, so these actions are associated with those users as well and
stored in the action log 220.
The action log 220 may be used by the social networking system 140
to track user actions on the social networking system 140, as well
as actions on third party systems 130 that communicate information
to the social networking system 140. Users may interact with
various objects on the social networking system 140, and
information describing these interactions is stored in the action
log 220. Examples of interactions with objects include: commenting
on posts, sharing links, checking-in to physical locations via a
mobile device, accessing content items, and any other suitable
interactions. Additional examples of interactions with objects on
the social networking system 140 that are included in the action
log 220 include: commenting on a photo album, communicating with a
user, establishing a connection with an object, joining an event,
joining a group, creating an event, authorizing an application,
using an application, expressing a preference for an object
("liking" the object), and engaging in a transaction. Additionally,
the action log 220 may record a user's interactions with
advertisements on the social networking system 140 as well as with
other applications operating on the social networking system 140.
In some embodiments, data from the action log 220 is used to infer
interests or preferences of a user, augmenting the interests
included in the user's user profile and allowing a more complete
understanding of user preferences.
The action log 220 may also store user actions taken on a third
party system 130, such as an external website, and communicated to
the social networking system 140. For example, an e-commerce
website may recognize a user of a social networking system 140
through a social plug-in enabling the e-commerce website to
identify the user of the social networking system 140. Because
users of the social networking system 140 are uniquely
identifiable, e-commerce websites, such as in the preceding
example, may communicate information about a user's actions outside
of the social networking system 140 to the social networking system
140 for association with the user. Hence, the action log 220 may
record information about actions users perform on a third party
system 130, including webpage viewing histories, advertisements
that were engaged, purchases made, and other patterns from shopping
and buying.
In one embodiment, the edge store 225 stores information describing
connections between users and other objects on the social
networking system 140 as edges. Some edges may be defined by users,
allowing users to specify their relationships with other users. For
example, users may generate edges with other users that parallel
the users' real-life relationships, such as friends, co-workers,
partners, and so forth. Other edges are generated when users
interact with objects in the social networking system 140, such as
expressing interest in a page on the social networking system 140,
sharing a link with other users of the social networking system
140, and commenting on posts made by other users of the social
networking system 140.
In one embodiment, an edge may include various features each
representing characteristics of interactions between users,
interactions between users and objects, or interactions between
objects. For example, features included in an edge describe rate of
interaction between two users, how recently two users have
interacted with each other, the rate or amount of information
retrieved by one user about an object, or the number and types of
comments posted by a user about an object. The features may also
represent information describing a particular object or user. For
example, a feature may represent the level of interest that a user
has in a particular topic, the rate at which the user logs into the
social networking system 140, or information describing demographic
information about a user. Each feature may be associated with a
source object or user, a target object or user, and a feature
value. A feature may be specified as an expression based on values
describing the source object or user, the target object or user, or
interactions between the source object or user and target object or
user; hence, an edge may be represented as one or more feature
expressions.
The edge store 225 also stores information about edges, such as
affinity scores for objects, interests, and other users. Affinity
scores, or "affinities," may be computed by the social networking
system 140 over time to approximate a user's interest in an object
or in another user in the social networking system 140 based on the
actions performed by the user. A user's affinity may be computed by
the social networking system 140 over time to approximate the
user's interest in an object, a topic, or another user in the
social networking system 140 based on actions performed by the
user. Computation of affinity is further described in U.S. patent
application Ser. No. 12/978,265, filed on Dec. 23, 2010, U.S.
patent application Ser. No. 13/690,254, filed on Nov. 30, 2012,
U.S. patent application Ser. No. 13/689,969, filed on Nov. 30,
2012, and U.S. patent application Ser. No. 13/690,088, filed on
Nov. 30, 2012, each of which is hereby incorporated by reference in
its entirety. Multiple interactions between a user and a specific
object may be stored as a single edge in the edge store 225, in one
embodiment. Alternatively, each interaction between a user and a
specific object is stored as a separate edge. In some embodiments,
connections between users may be stored in the user profile store
205, or the user profile store 205 may access the edge store 225 to
determine connections between users.
The advertisement ("ad") module 230 includes an advertisement
("ad") store 231, an ad auction store 233, an ad auction module
235, and a reach estimation module 240. The ad module 230 stores
information describing advertisement ("ad") campaigns received from
one or more advertisers. Each advertising campaign includes one or
more advertisements described by ad requests, with information
describing an advertising campaign and its constituent
advertisements included in the ad store 231.
An advertising campaign may be associated with one or more
objectives, a duration, and a budget. An objective associated with
an advertising campaign describes one or more goals for actions
associated with the advertising campaign. For example, an objective
specifies a total number of impressions of advertisements in the
advertising campaign to social networking system users (an
"impression objective"), specifies a total number of unique social
networking system users to be presented with at least one
advertisement from the advertising campaign (a "reach objective"),
specifies a number of times an advertisement from the advertising
campaign is presented to a social networking system user (a
"frequency objective"), or specifies any other suitable criteria.
The budget specifies a total amount of compensation an advertiser
associated with an advertising campaign provides the social
networking system 140 for presenting advertisements in the
advertising campaign or for the social networking system 140
receiving interactions with advertisements in the advertising
campaign. Additionally, the duration associated with the
advertising campaign specifies a time interval during which
advertisements from the advertising campaign are presented to
social networking system users. For example, if the duration of an
advertising campaign is 30 days, advertisements included in the
advertising campaign are presented to social networking system
users for 30 days after the advertising campaign is provided to the
social networking system 140. In some embodiments, the advertiser
may also specify a start date for the advertising campaign, so the
duration is measured from the specified start date.
An advertising campaign includes one or more ad requests that each
describe advertisements in the advertising campaign for
presentation to one or more social networking system users. An ad
request includes advertisement content (also referred to as an
"advertisement"), which is text, image, audio, video, or any other
suitable data presented to a user. In various embodiments, the
advertisement content is associated with a network address
specifying a landing page, or other destination, to which a user is
directed when the advertisement is accessed. An ad request also
associates a bid amount with an advertisement. In some embodiments,
the bid amount is specified by an advertiser and is used to
determine an expected value, such as monetary compensation provided
by an advertiser, to the social networking system 140 if the
advertisement is presented to a user, if the advertisement receives
a user interaction, or based on any other suitable condition. For
example, the bid amount specifies a monetary amount that the social
networking system 140 receives from the advertiser if the
advertisement is displayed and the expected value is determined by
multiplying the bid amount by a probability of the advertisement
being accessed.
Alternatively, the advertiser does not associate a bid amount with
the ad request, but specifies a budget, a duration and/or one or
more objectives with an advertising campaign including the ad
request. Based on the budget or one or more objectives associated
with the advertising campaign including the ad request and based on
previously completed auctions selecting advertisements for a user
the social networking system 140 determines a bid amount associated
with the ad request. For example, based on a reach objective of an
advertising campaign, the social networking system 140 analyzes
previously completed advertisement auctions selecting an
advertisement using different potential bid amounts to identify a
bid amount for advertisements in the advertising campaign to
accomplish the reach objective.
Additionally, an advertisement request may include one or more
targeting criteria specified by the advertiser. Targeting criteria
included in an advertisement request specify one or more
characteristics of users eligible to be presented with
advertisement content in the advertisement request. For example,
targeting criteria are used to identify users having user profile
information, edges, or actions satisfying at least one of the
targeting criteria. Hence, targeting criteria allow an advertiser
to identify users having specific characteristics, simplifying
subsequent distribution of content to different users.
Additionally, targeting criteria may be associated with an
advertising campaign in its entirety, so multiple advertisements in
the advertising campaign are eligible for presentation to users
having characteristics satisfying one or more of the targeting
criteria associated with the advertising campaign.
In one embodiment, targeting criteria may specify actions or types
of connections between a user and another user or object of the
social networking system 140. Targeting criteria may also specify
interactions between a user and objects performed external to the
social networking system 140, such as on a third party system 130.
For example, targeting criteria identifies users that have taken a
particular action (e.g., sending a message to another user, using
an application, joining a group, leaving a group, joining an event,
generating an event description, purchasing or reviewing a product
or service using an online marketplace, requesting information from
a third-party system 130, or any other suitable action). Including
actions in targeting criteria allows advertisers to further refine
users eligible to be presented with advertisement content from an
advertisement request. As another example, targeting criteria
identifies users having a connection to another user or object or
having a particular type of connection to another user or
object.
Targeting criteria may also specify a location associated with
users eligible to be presented with advertisement content in an
advertisement request. For instance, a targeting criteria specifies
that advertisement content included in an advertisement request is
eligible for presentation to users associated with a location
within a threshold distance from San Francisco and not eligible for
presentation to users associated with locations greater than
threshold distance from San Francisco. A location specified in
targeting criteria may be based on the location of a residence
associated with a user, a location of a workplace associated with
the user, a current location of the user (e.g., a location
determined by a global positioning system GPS receiver of a mobile
device of the user), or any other suitable location associated with
a user.
The ad auction store 233 includes information describing previously
completed auctions where one or more advertisements were selected.
For example, the ad auction store 233 includes information
identifying a user for which a previously completed auction
selected an advertisement, the advertisement selected from the
previously completed auction, a bid amount associated with the
selected advertisement, an amount charged to an advertiser
associated with the selected advertisement, and a context scalar
describing a context in which the selected advertisement was
presented based on an advertisement auction (e.g., based on the
user, based on a location of ad placement, based on a type of
computing device used to view the ad, and the like). In various
embodiments, additional information describing an ad auction may
also be stored, such as targeting criteria associated with the
selected advertisement.
The ad auction module 235 retrieves advertisement requests from one
or more advertising campaigns and ranks the received advertisements
based on their bid amounts when an opportunity to present an
advertisement to a user is identified. For example, the ad auction
module 235 ranks the received advertisement requests based on their
associated expected values, which are based at least in part on the
advertisement requests' bid amounts, where advertisement requests
associated with larger expected values have a higher position in
the ranking. The ad auction module 235 selects one or more
advertisements from advertisement requests based on the ranking,
with the selected advertisements are presented to a social
networking system user. For example, the ad auction module 235
selects advertisements from advertisement requests having the
highest positions in the ranking or having at least a threshold
position in the ranking. In other embodiments, the auction module
235 may use any other suitable method to select advertisements for
presentation to a social networking system user.
The reach estimation module 240 estimates the number of distinct
users associated with locations within a certain geographic region
within a given time period. The reach estimation module 240 may be
used to estimate the number of users that would be able to reach by
an ad campaign that is targeted to users located in a specific
geographic area. In the example of FIG. 2, the reach estimation
module 240 includes a user sampling module 250, a location store
255, a geo-tile user counting module 260, and a sampling rate
determination module 265. However, in other embodiments, the reach
estimation module 240 may include different and/or additional
components than those described in conjunction with FIG. 2.
The user location sampling module 250 associates a location with a
user of the social networking system. In one embodiment, an
association between the location and the user is stored in the
location store 255. As illustrated in FIG. 3, the location store
255 includes information identifying a user, information
identifying a location associated with the user, and a time stamp
indicating when the location was associated with the user. For
example, the location store 255 includes a user identifier,
information identifying a location associated with the user
identifier, and a time stamp indicating the date and/or time at
which the location was obtained for the user corresponding to the
user identifier. In one embodiment, the user location sampling
module 250 stores a latitude and longitude of a location of the
user in the location store 255.
Alternatively, the user location sampling module 250 divides a
geographic region into a plurality of geo-tiles that each
correspond to a geographic area in the geographic region and stores
a geo-tile identification (geo-tile ID) of the location of the
user, which identifies a geo-tile including a location associated
with the user in the location store 255. Each location included in
a specific geo-tile is assigned to a geo-tile ID associated with
the geo-tile. Hence, different geographic locations included in a
geographic area corresponding to a geo-tile are associated with a
common geo-tile ID associated with the geo-tile. In some
embodiments, a geo-tile ID is determined based on a halving
algorithm that reduces the area of a geo-tile by a factor of 2 (or
a power of 2, such as 4) each time the level of precision of the
geo-tile ID algorithm is increased; when the geo-tile ID algorithm
precision is increased, each geo-tile corresponds to a smaller
geographic area, allowing a specific geo-tile to more specifically
identify locations. In other embodiments, the halving algorithm
reduces the latitude and/or longitude range of a geo-tile by a
factor of 2 (or a power of 2) each time the level of precision of
the geo-tile ID in increased so each geo-tile corresponds to a
smaller geographic area.
In some embodiments, a level of precision of the geo-tile ID
algorithm is represented by the number of digits or characters of
the geo-tile ID. That is, a geo-tile ID with a level of precision
of 15 has 15 digits or characters. In some embodiments, each digit
of a geo-tile ID is a binary digit, so the geo-tile ID is a binary
value. In these embodiments, when the level of precision of the
geo-tile ID algorithm is increased by one (i.e., an additional
digit is included in the geo-tile ID), the latitude or longitude
range included in a geo-tile is reduced by a factor of 2 depending
on whether the digit is in an odd position or an even position. In
another embodiment, the digits of a geo-tile ID are hexadecimal
digits. In this embodiment, when the level of precision of the
geo-tile ID algorithm is increased by one (i.e., an additional
digit is included in the geo-tile ID), the latitude and longitude
range of locations included in a geo-tile are each reduced by a
factor of 4.
Alternatively, the level of precision of a geo-tile ID value is a
fraction of the number of digits or characters comprising the
geo-tile ID value. Thus, multiple digits are added to a geo-tile ID
value to increase the level of precision of the geo-tile ID. For
example, to increase the level of precision of a geo-tile ID by
one, two binary digits are appended to the geo-tile ID value.
A geo-tile at a lower level of precision includes multiple
geo-tiles at higher levels of precision. To identify a geo-tile
having a lower-level of precision that includes a geo-tile, one or
more least-significant digits or characters may be removed from a
geo-tile ID to identify a geo-tile ID of the less precise geo-tile.
For example, if a geo-tile ID has a level of precision of 15,
removing the least-significant digit of the geo-tile ID yields the
geo-tile ID corresponding to a geo-tile having a level of precision
of 14 that includes the geo-tile corresponding toe the geo-tile ID
having the level of precision of 15. As a specific example, if a
geo-tile ID for a user location is 1011001110101010, a geo-tile ID
of a geo-tile including the geo-tile corresponding to the geo-tile
ID of 1011001110101010 is 10110011101010. In other embodiments,
other algorithms may be used to generate the geo-tile IDs. Example
methods for dividing a geographic region into a plurality of
geo-tiles and generating geo-tile IDs for the plurality of
geo-tiles are further described in U.S. patent application Ser. No.
13/685,246, filed Nov. 26, 2012, and titled "Search Results Using
Density-Based Map Tiles;" and U.S. patent application Ser. No.
14/220,493, filed Mar. 20, 2014, and titled "Polygon-Based Indexing
of Places," both of which are hereby incorporated by reference in
their entirety.
Referring back to FIG. 2 and FIG. 3, in various embodiments, the
user location sampling module 250 stores a single location
associated with each user in the location store 255 based on
privacy settings enforced by the social networking system 140 and
periodically updates the location associated with a user. In some
embodiments, the user location sampling module 250 updates the
location of a user within a specified time interval. To reduce the
number of times the location associated with a user is updated, the
user location sampling module 250 does not update the location
associated each time a new location associated with the user is
received by the social networking system 140. When a new location
associated with a user is a received by the social networking
system 140 (e.g., received from a client device 110 associated with
the user), the user location sampling module 250 determines whether
to update the location associated with the user. Whether to update
the location associated with the user is based at least in part on
the number of times a location associated with the user has been
received in the specified time interval (e.g., the number of times
location associated with the user is received within a 7 day period
of time). For example, each time a location associated with the
user is received, the user location sampling module 250 determines
whether to update the location of the user with a probability of
1/n, where n is the average number of times a location associated
with the user is received during a specified time interval (e.g.,
received within 7 days). The average number of times a location
associated with the user is received is determined based on
historical data associated with the user. For example, the average
number of times a location associated with the user is received may
be determined based on the number of times a location associated
with the user was received during a time period, such as 30
days.
If each time a location associated with the user is received, the
user location sampling module determines whether to update the
location with a probability of 1/n, so after n locations associated
with a user are received, the probability that the location
associated with the user is not updated,
(P.sub.no.sub._.sub.update), is:
.times..times..times..times. ##EQU00001## For example, if n is 2,
the probability that the location of the user will not be updated
after n locations are received is 1/4 or 25%. In another example,
if n is 3, the probability that the location of the user will not
be updated after n locations are received is (2/3).sup.3 or 29.6%.
Hence, as n increases, the probability that the location is not
updated also increases. Furthermore, as n increases, the
probability that the location associated with the user is not
updated approaches 1/e.apprxeq.36.8%. That is:
>.infin..times..times..times..times..times..times.>.infin..times..t-
imes. ##EQU00002##
To increase the probability that the location associated with a
user is updated within the specified time interval, the probability
used to determine whether to update the location associated with
the user may increase based on a number of times a location
associated with the user was received since a most recent update of
the location associated with the user, and/or the amount of time
that has elapsed between a current time and a most recent update of
the location associated with the user. FIG. 4 is a process flow
diagram illustrating one method for increasing a probability of
updating the location associated with a user. As illustrated in
FIG. 4, after updating a location associated with a user, the
probability used by the user location sampling module 250 to
determine whether to update the location associated with the user
is 1/n. Additionally, after receiving n locations associated with
the user, the probability used by the user location sampling module
250 to determine whether to update the location associated with the
user is 1. In some embodiments, the probability used by the user
location sampling module 250 is:
##EQU00003## where n is the average number of times a location
associated with a user is received within a specified time
interval, t is a number of times a location associated with the
user was received after the location associated with the user was
most recently updated. When t is 1 (i.e., the first location
associated with a user received after the location associated with
the user was updated), the probability P.sub.1 is 1/n. When t is n
(i.e., the n-th location associated with the user received after
the location of the user was updated), the probability P.sub.n is
equal to 1.
Referring back to FIG. 2, the geo-tile user counting module 260
determines a number of distinct users associated with locations
within in a geo-tile during a specified time period (e.g., within
the past 7 days). To determine the number of distinct users
associated with a location within a geo-tile, the geo-tile user
counting module 260 stores information related to locations
associated with various users and received form client devices 110
associated with the various users in the location store 255. To
protect the privacy of social networking system users, the geo-tile
user counting module 260 stores a hash value (e.g., a cryptographic
hash function such as the secure hash algorithm (SHA), or the
message-digest algorithm (MD5)) of a string based at least in part
on a location associated with a user and information identifying
the user. FIG. 5 shows an example of information stored by the
geo-tile user counting module 260 in the location store 255. In the
example of FIG. 5, the geo-tile user counting module 260 stores a
time stamp 501 of when a location associated with a user was
received, information identifying the received location 503, and
cryptographic hash value 505 of a string based at least in part on
a location of a user and information identifying the user.
The geo-tile user counting module 260 stores multiple entries, each
entry having a different level of precision of the information
identifying a received location associated with a user. For
example, the geo-tile user counting module 260 stores multiple
entries each having a different level of precision of the geo-tile
ID algorithm that generates a geo-tile identification (geo-tile ID)
including a received location associated with a user. The example
table of FIG. 5 includes entries for a first location included in
geo-tile 12345678 for user1, entries for a second location included
in geo-tile 12345679 for user2, and entries for a third location
included in geo-tile 12345688 for user1. In the example of FIG. 5,
the geo-tile user counting module 260 stores entries at four levels
of precision. Hence, for the first location included in geo-tile
12345678, the geo-tile user counting module 260 stores a first
entry 510A associated with geo-tile 12345678, a second entry 510B
associated with geo-tile 1234567, which is a geo-tile having a
lower level of precision than geo-tile 12345678 and including
geo-tile 12345678, a third entry 510C associated with geo-tile
123456, which includes geo-tile 1234567, and a fourth entry 510D
associated with the geo-tile 12345, which includes geo-tile 123456.
Similarly, entries 520A-520D identify a location associated with
geo-tile 12345679 for user2 at different levels of precision, while
entries 530A-530D identify a location associated with geo-tile
12345688 for user1 at different levels of precision.
In some embodiments, the geo-tile user counting module 260
determines whether a hash value 505 for a received location
associated with a user is already stored in the location store 255.
If the hash value 505 is already stored in the location store 255,
the geo-tile user counting module 260 updates the time stamp of the
stored entry in the location store 255 with the time stamp of the
received location. Referring to FIG. 5, in this embodiment, since
entries 510C and 530C have the same hash value and entries 510D and
530D have the same hash value 505, instead of storing new entries
for entries 530C and 530D, the geo-tile user counting module 260
updates entries 510C and 510D with a time stamp corresponding to a
time when information identifying the locations corresponding to
hash values 505 included in entries 530C and 530D was received.
To determine a number of users associated with a location included
in a geographic area corresponding to a geo-tile, the geo-tile user
counting module 260 determines a number of different hash values
505 associated with the geo-tile. For example, the geo-tile user
counting module 260 determines a number of entries in the location
store 255 that are associated with the specific geo-tile and
associated with distinct hash values 505. As information
identifying the user is hashed in combination with information
identifying the location of the user using a cryptographic hashing
function to determine a hash value 505, the social networking
system 140 is unable to determine prior locations associated with a
user based on information identifying the user. At most, given a
location and information identifying a user, the social networking
system 140 may determine whether a previously-received location
associated with the user was included in a geo-tile associated with
the given location.
Referring back to FIG. 2, the sampling rate determination module
265 determines a rate at which the user location sampling module
250 samples user locations for a specific geo-tile. For a specific
geo-tile, the sampling rate determination module 265 determines a
ratio between a number of users associated with a location included
in the geo-tile by the user location sampling module 250 and a
number of distinct users associated with locations included in the
geo-tile determined by the geo-tile user counting module 260. For
example, a number of users with stored locations located inside the
geo-tile is divided by a number of unique hash values associated
with locations included in the geo-tile. Additionally, the sampling
rate determination module 265 may also determine an average
sampling rate for multiple geo-tiles based on sampling rates
associated with each geo-tile. For example, geo-tiles included in a
geographic region are identified, and an average sampling rate for
the geographic region is determined based on sampling rates of each
of the identified geo-tiles.
The web server 270 links the social networking system 140 via the
network 120 to the one or more client devices 110, as well as to
the one or more third party systems 130. The web server 270 serves
web pages, as well as other content, such as JAVA.RTM., FLASH.RTM.,
XML and so forth. The web server 270 may receive and route messages
between the social networking system 140 and the client device 110,
for example, instant messages, queued messages (e.g., email), text
messages, short message service (SMS) messages, or messages sent
using any other suitable messaging technique. A user may send a
request to the web server 270 to upload information (e.g., images
or videos) that are stored in the content store 210. Additionally,
the web server 270 may provide application programming interface
(API) functionality to send data directly to native client device
operating systems, such as IOS.RTM., ANDROID.TM., WEBOS.RTM. or
BlackberryOS.
Reach Estimation
FIG. 6 illustrates a flow diagram of a process for estimating a
number of users associated with locations within a geographic
region. For example, the geographic region is a circular area
having a center at a location and a radius specified by the user.
In other embodiments, the process may include different and/or
additional steps than those described in conjunction with FIG. 6 or
may perform steps in different orders than the order described in
conjunction with FIG. 6.
The reach estimation module 240 receives 610 a description of a
geographic region. For example, the reach estimation module 240
receives a location and selects a radius 610 of circular geographic
region having a center at the received location. In some
embodiments, the reach estimation module 240 selects various radii
to define different circular geographic regions and repeats the
process described in conjunction with FIG. 6 for different
radii.
Referring to FIG. 7, an example geographic region comprising a
circle 710 with a selected radius is shown. The reach estimation
module 240 selects 615 geo-tiles 720 included in the geographic
region. For example, the reach estimation module 240 selects 615
geo-tiles based on a radius associated with the specified
geographic region. In one embodiment, a set of geo-tiles that
approximate the circle 710 with the selected radius is selected
615. For example, geo-tiles having at least a threshold area within
the circle 710 are selected 615. In some embodiments, the geo-tiles
720 included in the selected set of geo-tiles have the highest
level of precision used by the reach estimation module 240. To
select 615 geo-tiles, the reach estimation module 240 selects 615
geo-tiles having the highest level of precision that overlap with
the specified geographic region. Alternatively, geo-tiles having at
least a threshold amount of overlap with the specified geographic
region are selected 615. In some embodiments, if all geo-tiles
having a higher level of precision are included in the specified
geographic region, a geo-tile having a lower level of precision and
including the geo-tiles having the higher level of precision is
selected 615 instead of the more precise geo-tiles. In the example
of FIG. 8, a geo-tile having a lower level of precision 815
includes multiple geo-tiles having a higher level of precision 820
that are each included in a geographic region 810, so the geo-tile
having the lower level of precision 815 is selected 615, as well as
geo-tiles having the higher level of precision 820 that are
included in the geographic area 810 (or geo-tiles having the higher
level of precision 820 that have at least a threshold area within
the geographic area 810). Hence, in some embodiments, such as the
example of FIG. 8, the selected set of geo-tiles that approximate a
geographic area includes geo-tiles having different levels of
precision.
The user location sampling module 250 determines 620 the number of
distinct users associated with locations included in each of the
selected geo-tiles 720. For each selected geo-tile, the user
location sampling module 250 determines users associated with a
location included in a selected geo-tile 720. For example, the
geo-tile user location sampling module 250 identifies users from
the location store 255 associated with location identification
information corresponding to a selected geo-tile 720. Referring to
FIG. 3, the geo-tile user location sampling module 250 identifies
information from the location store having location identification
information specifying a selected geo-tile 720. In some
embodiments, users associated with a location included in a
selected geo-tile and having characteristics satisfying one or more
targeting criteria are identified. For example, if a user specifies
targeting criteria associated with the geographic region (e.g.,
targeting criteria of an advertisement request for presentation to
users included in the geographic region) that identifies users
between 21 and 30 years old, the user location sampling module 250
determines 620 the number of distinct users associated with
locations included in each of the selected geo-tiles 720 and having
an age between 21 and 30.
Additionally, the sampling rate determination module 265 determines
625 an average sampling rate based on the selected geo-tiles 720.
For a specific geo-tile, the sampling rate determination module 265
determines a ratio between a number of users associated with a
location included in the geo-tile by the user location sampling
module 250 and a number of distinct users associated with locations
included in the geo-tile determined by the geo-tile user counting
module 260. For example, a number of users with stored locations
located inside the geo-tile is divided by a number of unique hash
values associated with locations included in the geo-tile to
determine the sampling rate for the geo-tile. In one embodiment,
the sampling rate determination module 265 determines an arithmetic
mean of the sampling rates of each of the selected geo-tiles 720 to
determine 625 the average sampling rate. Alternatively, the average
sampling rate is determined 625 as a weighted arithmetic mean of
sampling rates for selected geo-tiles 720. For example, sampling
rates of different selected geo-tiles 720 are weighted by a
percentage of the different geo-tiles that is included in the
geographic region. Alternatively, sampling rates associated with
different selected geo-tiles 720 are weighted based on a number of
users associated with locations included in each of the selected
geo-tiles 720. In some embodiments, sampling rates may be
determined for multiple subsets of the selected geo-tiles 720, and
the sampling rate determination module 265 determines the average
sampling rate based on the sampling rates determined for the
multiple subsets of the selected geo-tiles 720. For example, the
average sampling rate is an average of the sampling rates
determined for the multiple subsets of the selected geo-tiles
720.
Alternatively, a sampling rate may be determined 625 based on
geo-tiles with lower resolution than the resolution of the selected
geo-tiles 720. For example, based on the number of geo-tiles 720
that approximate the geographic region, such as the circle 710 in
FIG. 7, a geo-tile resolution is determined. For instance, the
geo-tile resolution is determined based on a logarithmic value of
the number of geo-tiles that approximate the geographic region. In
some embodiments, two geo-tile resolutions (res.sub.1 and
res.sub.2) are determined based on the logarithmic value of the
number of geo-tiles approximating the geographic region:
res.sub.1=res.sub.high-floor(res) (4)
res.sub.2=res.sub.high-ceil(res) (5) where
res=log.sub.4(num_small_tiles) (6) where num_small_tiles is the
number of geo-tiles 720 that approximate circle 710. In some
embodiments, the number of geo-tiles 720 that approximate the
circle 710 is determined as:
.times..times..times. ##EQU00004## In the example of FIG. 7, 24
selected geo-tiles 720 approximate circle 710. That is:
res.sub.1=res.sub.high-floor(log.sub.4(24))=res.sub.high-floor(2.29)=res.-
sub.high-2
res.sub.2=res.sub.high-ceil(log.sub.4(24))=res.sub.high-ceil(2.-
29)=res.sub.high-3 For instance, if res.sub.high is 15, res.sub.1
is equal to 13 and res.sub.2 is equal to 12.
Geo-tiles having the determined resolution that have at least a
threshold amount of overlap with at least one of the selected
geo-tiles 720 in the set of geo-tiles having the highest level of
precision (res.sub.high) are selected. As shown in FIG. 7,
geo-tiles 730A, 730B, 730C and 730D having level of precision
(res.sub.1) 2 levels lower than the level of precision of the
selected geo-tiles 720, and geo-tiles 740 having a level of
precision (res.sub.2) 3 levels lower than the selected geo-tiles
720 having the highest level of precision are selected.
An average sampling rate (sampling_rate_1) for the geo-tiles 730
having a level of precision res.sub.1 and an additional average
sampling rate (sampling_rate_2) for the geo-tiles 740 having a
level of precision res.sub.2 are determined. The sampling rate
determination module 265 determines an average of the average
sampling rate and the additional average sampling rate. In some
embodiments, the average of the average sampling rate and the
additional average sampling rate is weighted based on the number
selected geo-tiles that approximate the geographic region. For
instance, the average of the average sampling rate and the
additional average sampling rate is weighted based on the decimal
portion of the logarithmic value of the number of geo-tiles 720
that approximate the geographic region. In one embodiment, the
average sampling rate is determined as:
average_sampling_rate=(ceil(log.sub.4(num_small_tiles))-log.sub.4(num_sma-
ll_tiles)).times.sampling_rate_1+(log.sub.4(num_small_tiles)-floor(log.sub-
.4(num_small_tiles))).times.sampling_rate_2 (8)
Based on the number of distinct users associated with locations
included in each of the selected geo-tiles 720 and the average
sampling rate, the reach estimation module 240 estimates 630 the
number of distinct users associated with locations within the
geographic region. For instance, the reach estimation module 240
determines the number of distinct users associated with locations
within the geographic region as:
.times. ##EQU00005## Where sampled_users is the number of distinct
users associated with locations included in each of the selected
geo-tiles 720 and total_users is the estimated number of users
associated with a location within the geographic region.
The number of distinct users may be determined by the social
networking system 140 when a request is received from an
advertiser. For example, when providing an advertisement to the
social networking system 140 for presentation to users associated
with locations within a specified geographic region, the social
networking system 140 determines numbers of distinct users
associated with locations within different geographic regions and
presents the advertiser with the numbers of distinct users,
allowing the advertiser to modify the geographic region associated
with the advertisement. Alternatively, an advertiser specifies a
number of users to be presented with the advertisement, and the
social networking system 140 identifies a geographic region
including a number of distinct users equaling or exceeding the
specified number of users. The social networking system 140 may
then specify the geographic region to the user for association with
the advertisement.
Summary
The foregoing description of embodiments has been presented for the
purpose of illustration; it is not intended to be exhaustive or to
limit the patent rights to the precise forms disclosed. Persons
skilled in the relevant art can appreciate that many modifications
and variations are possible in light of the above disclosure.
Some portions of this description describe embodiments in terms of
algorithms and symbolic representations of operations on
information. These algorithmic descriptions and representations are
commonly used by those skilled in the data processing arts to
convey the substance of their work effectively to others skilled in
the art. These operations, while described functionally,
computationally, or logically, are understood to be implemented by
computer programs or equivalent electrical circuits, microcode, or
the like. Furthermore, it has also proven convenient at times, to
refer to these arrangements of operations as modules, without loss
of generality. The described operations and their associated
modules may be embodied in software, firmware, hardware, or any
combinations thereof.
Any of the steps, operations, or processes described herein may be
performed or implemented with one or more hardware or software
modules, alone or in combination with other devices. In one
embodiment, a software module is implemented with a computer
program product comprising a computer-readable medium containing
computer program code, which can be executed by a computer
processor for performing any or all of the steps, operations, or
processes described.
Embodiments may also relate to an apparatus for performing the
operations herein. This apparatus may be specially constructed for
the required purposes, and/or it may comprise a general-purpose
computing device selectively activated or reconfigured by a
computer program stored in the computer. Such a computer program
may be stored in a non-transitory, tangible computer readable
storage medium, or any type of media suitable for storing
electronic instructions, which may be coupled to a computer system
bus. Furthermore, any computing systems referred to in the
specification may include a single processor or may be
architectures employing multiple processor designs for increased
computing capability.
Embodiments may also relate to a product that is produced by a
computing process described herein. Such a product may comprise
information resulting from a computing process, where the
information is stored on a non-transitory, tangible computer
readable storage medium and may include any embodiment of a
computer program product or other data combination described
herein.
Finally, the language used in the specification has been
principally selected for readability and instructional purposes,
and it may not have been selected to delineate or circumscribe the
patent rights. It is therefore intended that the scope of the
patent rights be limited not by this detailed description, but
rather by any claims that issue on an application based hereon.
Accordingly, the disclosure of the embodiments of the invention is
intended to be illustrative, but not limiting, of the scope of the
patent rights, which is set forth in the following claims.
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